An Energy Management Strategy for Hybrid Energy Storage System using Reinforcement Learning

An enhanced Deep Deterministic Policy Gradient (DDPG) algorithm is proposed to improve the performance of hybrid energy storage system (HESS) energy management. The proposed method achieves rapid learning of high-performance strategies by introducing a dynamic incentive term to the reward function and a simple but useful categorized experience replay mechanism. Experimental results demonstrate that the algorithm converges to an effective policy within 20 training episodes, significantly enhancing control performance and learning efficiency compared to baseline methods.